Examining interviewer bias in medical school admissions: The interplay between applicant and interviewer gender and its effects on interview outcomes

Selection interviews have long been integral to medical school admissions, yet their limited predictive validity and susceptibility to bias raise concerns. This study delves into potential interviewer bias within the dynamics of interviewee and interviewer gender. We analyze a dataset of 5,200 applicants and over 370 selection committees engaged in semi-structured interviews from 2006 to 2019 at a large German medical school with multiple linear and non-linear regression analyses. Our findings reveal that all-female committees tended to award male candidates, on average, one point more than their female counterparts, significantly enhancing the chances of submission for male applicants despite lower academic grades, which constituted 51% of the selection process points. All-male and mixed-gender committees exhibited similar ratings for both genders. The role of valuing voluntary services emerged prominently: all-male and mixed committees acknowledged women’s volunteer work but not men’s, while all-female committees demonstrated the opposite pattern. Our results attribute variations in interview outcomes to the absence of standardization, such as insufficient interviewer training, divergent rating strategies, variations in interviewer experience, and imbalances in candidate allocation to selection committees, rather than to a “gender bias”, for example by favoritism of males because of their gender.

, conditional on explanatory variables that may influence the outcome.
Our main predictors of interest include subjects' gender (a binary indicator if the interviewee was female or not) and the commission type j.The interaction between gender and commission type ( 3 ) is of particular importance, as it gives the predicted and adjusted interview score for CF (CM) conditional on whether she or he was interviewed by MM, MF, or FF.Since the ranking that determined whether an applicant was selected or not was formed based on the interview and the Abitur grade, we include the latter in the regression model.Other control variables include the waiting semester and a binary indicator for voluntary service.
We further include interview year fixed effects (FEt) to control for time-dependent trends or year-specific circumstances, such as increases in Abitur grades over time or commission type distribution.

Probit regressions for selection probabilities
Figure 4 in the main text is derived analogous to Figure 2.However, the continuous variable 'interview grade' is replaced with the binary indicator whether a study place was offered or not.Now, instead of estimating an OLS model, a non-linear probit model is employed.Thus, as a variation of eq. ( 1), we estimate the probability (Pr) of being selected (1 = yes, 0 = no) for subject i, conditional on the same predictors and the same vector X of explanatory variables that may influence the outcome, here Pr (selectionijt = 1 | X = x), as in eq. ( 1).
A full regression output is presented in Table A2 (probit coefficients) and Table A3 (partial effects).
Figure 5 in the main text illustrates the predictive probability of a successful interview at each Abitur grade.This is achieved by calculating partial effects of selectionijt = 1 with respect to commission type j at the observed Abitur grades using equation (3): Results for regressions using eq.( 3) are presented in columns (3-4) in Tables A2 and A3.

Table 3
in the main text is based on equation (1) with the variables described above.In TableA1below, we extend the model by including age and school type as additional covariates.

Table A1 :
Regression analysis for interview scores with additional covariates Notes: OLS regressions for the overall interview grade and the separate conversation topics.Robust standard errors are in parentheses.Year-fixed effects (FE) are jointly significant in all specifications (Wald-Test).See Table3in the main text for regression models excluding Age and School type.The underlying regression model is described in equation (1) of the Online Attachment.CM: candidate is male; CF: candidate is female; MM: all-male selection committee; MF: mixed-gender selection committee; FF: all-female committee.*: p<0.05

Table A3 :
Probit regressions for Selection = 1 marginal effects are reported The numbers reported in this table are partial derivatives of the dependent variable with respect to all parameters and interactions reported in TableA2.These marginal effects can be interpreted as the response of the dependent variable in percentagepoints to a binary explanatory variable being = 1, or the response of the dependent variable in percent for continuous explanatory variables.Robust standard errors are in parentheses.Further table notes from TableA2apply here as well.*: p<0.05